IX. Mine Patterns

Build institutional memory

Solutions are expendable. Patterns are valuable.

The Problem

Without Pattern Mining

Same problems rediscovered repeatedly
Solutions locked in individual sessions
Zero knowledge transfer across contexts
Institutional memory decays
Waste time re-learning what was already learned

With Pattern Mining

Patterns documented and reusable
One pattern used 90% of the time (massive value)
Knowledge networks where learnings compound
374 hours saved from top pattern alone
Public patterns benefit 1000x more than private

The Solution

Lost Knowledge

Session 1: Discovers solution to problem X Session 50: Problem X again -> rediscover Session 100: Problem X again -> rediscover

Result: Zero knowledge transfer, same learning repeated endlessly

Ephemeral solutions. No patterns. No compound returns.

Pattern Extraction

Session 1: Discover solution -> Extract pattern Session 50: Problem X -> Reference pattern Session 100: Problem X -> Reference pattern

Result: Pattern reused 100x times, knowledge compounds

Reusable patterns. Documented. Compounding returns.

What Makes a Pattern

Every pattern captures these elements:

Problem

Challenge that occurs repeatedly

Context

When/where it occurs

Solution

How to solve it

Consequences

Results from applying

Pattern Extraction Process

::: info From Experience to Reusable Knowledge

Agent session completes
    |
Identify reusable insights
    |
Generalize from specific case
    |
Document as pattern
    |
Publish to pattern library
    |
Reference in future sessions
    |
Pattern compounds across uses

:::

Real Pattern Data

::: code-group

Month 0:  0 patterns
Month 3:  5 patterns (early discoveries)
Month 6:  15 patterns (detection improving)
Month 12: 35 patterns (steady growth)
Month 24: 52 patterns (mature, stable)

Top 5 patterns: Used in 90% of sessions
Top 10 patterns: Used in 75% of sessions
Top 20 patterns: Used in 50% of sessions

Example: Phase-Based Workflow
- Used in: 187 sessions (91%)
- Time savings: ~2 hours per session
- Total saved: 374 hours

Specific (not reusable):
"PostgreSQL StatefulSet with 3 replicas
and fast-ssd storage for production"

Pattern (reusable):
"For stateful services requiring persistence
and high availability, use StatefulSets
with replicated storage"

Applies to: PostgreSQL, MySQL, Redis, MongoDB

:::

Pattern Categories

Workflow Patterns

# Pattern: Phase-Based Workflow

## Problem
Monolithic workflows become unmanageable

## Solution
Break into Research -> Plan -> Implement phases
Each phase has clear deliverables
Human gates between phases

## Evidence
- Used in: 187 sessions
- Success rate: 95%
- Time savings: 2 hours/session

Technical Patterns

# Pattern: Context Bundles

## Problem
Multi-day work exceeds context limits

## Solution
Compress session state to 5-10% of original
Save as checkpoint bundle
Load when resuming work

## Evidence
- Compression ratio: 5:1 to 10:1
- Enabled: 45 multi-day sessions
- Previously: impossible

Implementation

Automated Pattern Detection

class PatternDetector:
    def detect_patterns(self, git_history):
        # Analyze commit messages
        commits = load_commits(git_history)

        # Extract "Learning:" sections
        learnings = []
        for commit in commits:
            if "Learning:" in commit.message:
                learnings.append(parse_learning(commit))

        # Cluster similar learnings
        clusters = cluster_similar(learnings)

        # Pattern emerges from 3+ occurrences
        patterns = []
        for cluster in clusters:
            if len(cluster) >= 3:
                patterns.append({
                    'name': cluster.theme,
                    'occurrences': len(cluster),
                    'evidence': cluster.commits
                })

        return patterns

Pattern Template

Section	Purpose
Problem	What challenge does this solve?
Context	When/where does this occur?
Solution	How do you solve it?
Implementation	Code/config examples
Consequences	Benefits, tradeoffs, risks
Related Patterns	Links to other patterns
Evidence	Metrics, validation, proof

Validation

You're doing this right if:

Every session extracts at least one learning
Patterns documented within 24 hours
Patterns reused across multiple sessions
Library grows steadily (not explosive)
Patterns have evidence (not theoretical)

You're doing this wrong if:

Sessions complete without learnings extracted
Patterns undocumented or poorly documented
Patterns never reused (too specific)
Pattern explosion (hundreds of trivial patterns)
Patterns without evidence (speculation)

Pattern Evolution

Patterns mature through stages:

1. Discovery

Problem found, solution works

Evidence: 1 session

2. Validation

Applied in 2-3 contexts

Evidence: 3+ sessions

3. Generalization

Abstracted and reusable

Evidence: 5+ contexts

4. Standard

Widely adopted

Evidence: ›80% adoption

::: info Network Effects Private patterns:

Your team: 10 people
Uses: 10x
Impact: 10x value

Public patterns:

Your team: 10 people
Community: 1000 people
Uses: 1000x
Impact: 1000x value
Contributions: Community improves your patterns
Result: Compound returns

Open source your learnings :::

Factor	Relationship
I. Automated Tracking	Patterns extracted from git history
III. Focused Agents	Agent composition patterns
V. Measure Everything	Telemetry reveals which patterns work
VI. Resume Work	Bundles are a pattern for continuity
VII. Smart Routing	Routing learns from pattern success rates